US 7702048 B2 Abstract A receiver is configured for canceling intra-cell and inter-cell interference in coded, multiple-access, spread-spectrum transmissions that propagate through frequency-selective communication channels. The receiver employs iterative symbol-estimate weighting, subtractive cancellation with a stabilizing step-size, and mixed-decision symbol estimates. Receiver embodiments may be implemented explicitly in software or programmed hardware, or implicitly in standard Rake-based hardware either within the Rake (i.e., at the finger level) or outside the Rake (i.e., at the user or subchannel symbol level).
Claims(9) 1. An interference canceller configured for performing at least one iteration for each of a plurality of input symbol estimates for producing updated interference-cancelled symbol estimates, the canceller comprising a weighting module, the weighting module configured to apply at least one symbol weight to the plurality of input symbol estimates, the at least one symbol weight comprising a function of an input symbol merit, wherein the canceller is configured to measure the input symbol merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the plurality of input symbol estimates and proximity of at least one of the plurality of input symbol estimates to a nearby constellation point, and wherein the function of the average ratio of signal power to interference-plus-noise power is substantially characterized by
where γ
^{[i]} is a symbol weight after an i^{th }iteration of the interference canceller, max { } is a function for selecting a maximum value from a set of quantities within brackets { }, SINR^{[i]} denotes an average ratio of signal power to interference-plus-noise power (SINR) of the symbol after the i^{th }iteration of the interference canceller, and C is a non-negative real constant for enforcing a minimum symbol weight.2. An interference canceller configured for performing at least one iteration for each of a plurality of input symbol estimates for producing updated interference-cancelled symbol estimates, the canceller comprising a weighting module, the weighting module configured to apply at least one symbol weight to the plurality of input symbol estimates, the at least one symbol weight comprising a function of an input symbol merit, wherein the canceller is configured to measure the input symbol merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the plurality of input symbol estimates and proximity of at least one of the plurality of input symbol estimates to a nearby constellation point, and wherein the canceller is configured to employ time-series averaging for calculating the proximity as a statistical average.
3. An interference canceller configured for performing at least one iteration for each of a plurality of input symbol estimates for producing updated interference-cancelled symbol estimates, the canceller comprising a weighting module, the weighting module configured to apply at least one symbol weight to the plurality of input symbol estimates, the at least one symbol weight comprising a function of an input symbol merit wherein the at least one symbol weight is substantially characterized by:
where γ
^{[i]} is a symbol weight after an i^{th }iteration of the interference canceller, {circumflex over (b)}^{[i]} is a symbol decision after the i_{th }iteration of the interference canceller, slice({circumflex over (b)}^{[i]}) represents the quantization of {circumflex over (b)}^{[i]} to a nearest constellation point, Re{ } returns a real part of an argument, E[ ] represents a statistical expectation or its estimate with a time average, ∥ represents the magnitude of a complex quantity, and * denotes the conjugate of a complex quantity.4. An interference cancellation method employing at least one iteration for each of a plurality of input symbol estimates for converting the plurality of input symbol estimates into updated interference-cancelled symbol estimates, wherein each of the at least one iteration comprises applying at least one symbol weight to the plurality of input symbol estimates, the method further comprising:
providing for calculating the at least one symbol weight from a function of an input symbol merit,
wherein providing for calculating the at least one symbol weight comprises measuring the input symbol merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the plurality of input symbol estimates and proximity of at least one of the plurality of input symbol estimates to a nearby constellation point, and
wherein the function of the average ratio of signal power to interference-plus-noise power is substantially characterized by
where γ
^{[i]} is a symbol weight after an i^{th }iteration of the interference canceller, max{ } is a function for selecting a maximum value from a set of quantities within brackets { }, SINR^{[i]} denotes an average ratio of signal power to interference-plus-noise power (SINR) of the symbol after the i^{th }iteration of the interference canceller, and C is a non-negative real constant for enforcing a minimum symbol weight.5. An interference cancellation method employing at least one iteration for each of a plurality of input symbol estimates for converting the plurality of input symbol estimates into updated interference-cancelled symbol estimates, wherein each of the at least one iteration comprises applying at least one symbol weight to the plurality of input symbol estimates, the method further comprising:
providing for calculating the at least one symbol weight from a function of an input symbol merit,
wherein providing for calculating the at least one symbol weight comprises measuring the input symbol merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the plurality of input symbol estimates and proximity of at least one of the plurality of input symbol estimates to a nearby constellation point, and
comprising providing for time-series averaging for calculating the proximity as a statistical average.
6. An interference cancellation method employing at least one iteration for each of a plurality of input symbol estimates for converting the plurality of input symbol estimates into updated interference-cancelled symbol estimates, wherein each of the at least one iteration comprises applying at least one symbol weight to the plurality of input symbol estimates, the method further comprising:
providing for calculating the at least one symbol weight from a function of an input symbol merit,
wherein the at least one symbol weight is substantially characterized by:
where γ
^{[i]} is a symbol weight after an i^{th }iteration of the interference canceller, {circumflex over (b)}^{[i]} is a symbol decision after the i^{th }iteration of the interference canceller, slice({circumflex over (b)}^{[i]}) represents the quantization of {circumflex over (b)}^{[i]} to a nearest constellation point, Re{ } returns a real part of an argument, E[ ] represents a statistical expectation or its estimate with a time average, ∥ represents the magnitude of a complex quantity, and * denotes the conjugate of a complex quantity.7. An interference cancellation system configured for converting input symbol estimates into updated interference-cancelled symbol estimates, wherein signal processing in each of at least one iteration for each of the input symbol decisions is performed by a weighting means configured for applying at least one symbol weight to the input symbol estimates, the system further comprising:
a weight-calculation means configured for calculating the at least one symbol weight from a function of a merit of an input symbol,
wherein the weight-calculation means is configured to measure the merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the input symbol estimates and proximity of at least one of the input symbol estimates to a nearby constellation point, and
wherein the function of the average ratio of signal power to interference-plus-noise power is substantially characterized by
where γ
^{[i]} is a symbol weight after an i^{th }iteration of the interference canceller, max { } is a function for selecting a maximum value from a set of quantities within brackets { }, SINR^{[i]} denotes an average ratio of signal power to interference-plus-noise power (SINR) of the symbol after the i^{th }iteration of the interference canceller, and C is a non-negative real constant for enforcing a minimum symbol weight.8. An interference cancellation system configured for converting input symbol estimates into updated interference-cancelled symbol estimates, wherein signal processing in each of at least one iteration for each of the input symbol decisions is performed by a weighting means configured for applying at least one symbol weight to the input symbol estimates, the system further comprising:
a weight-calculation means configured for calculating the at least one symbol weight from a function of a merit of an input symbol,
wherein the weight-calculation means is configured to measure the merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the input symbol estimates and proximity of at least one of the input symbol estimates to a nearby constellation point, and
wherein the weight-calculation means is configured to employ time-series averaging for calculating the proximity as a statistical average.
9. An interference cancellation system configured for converting input symbol estimates into updated interference-cancelled symbol estimates, wherein signal processing in each of at least one iteration for each of the input symbol decisions is performed by a weighting means configured for applying at least one symbol weight to the input symbol estimates, the system further comprising:
a weight-calculation means configured for calculating the at least one symbol weight from a function of a merit of an input symbol,
wherein the at least one symbol weight is substantially characterized by:
where γ
^{[i]} is a symbol weight after an i^{th }iteration of the interference canceller, {circumflex over (b)}^{[i]} is a symbol decision after the i^{th }iteration of the interference canceller, slice({circumflex over (b)}^{[i]}) represents the quantization of {circumflex over (b)}^{[i]} to a nearest constellation point, Re{ } returns a real part of an argument, E[ ] represents a statistical expectation or its estimate with a time average, ∥ represents the magnitude of a complex quantity, and * denotes the conjugate of a complex quantity.Description This application claims priority to Provisional U.S. Pat. Appl. Ser. No. 60/736,204, filed Nov. 15, 2005, and entitled “Iterative Interference Cancellation Using Mixed Feedback Weights and Stabilizing Step Sizes,” which is incorporated by reference in its entirety. 1. Field of the Invention The present invention relates generally to iterative interference cancellation in received wireless communication signals and, more particularly, to cancellation of intra-cell interference and/or inter-cell interference in coded spread spectrum communication systems. 2. Discussion of the Related Art In an exemplary wireless multiple-access system, a communication resource is divided into code-space subchannels that are allocated to different users. A plurality of subchannel signals received by a wireless terminal (e.g., a subscriber unit or a base station) may correspond to different users and/or different subchannels allocated to a particular user. If a single transmitter broadcasts different messages to different receivers, such as a base station in a wireless communication system broadcasting to a plurality of mobile terminals, the channel resource is subdivided in order to distinguish between messages intended for each mobile. Thus, each mobile terminal, by knowing its allocated subchannel(s), may decode messages intended for it from the superposition of received signals. Similarly, a base station typically separates received signals into subchannels in order to differentiate between users. In a multipath environment, received signals are superpositions of time-delayed and complex-scaled versions of the transmitted signals. Multipath can cause several types of interference. Intra-channel interference occurs when the multipath time-delays cause subchannels to leak into other subchannels. For example, in a forward link, subchannels that are orthogonal at the transmitter may not be orthogonal at the receiver. When multiple base stations (or sectors or cells) are active, there may also be inter-channel interference caused by unwanted signals received from other base stations. Each of these types of interference can degrade communications by causing a receiver to incorrectly decode received transmissions, thus increasing a receiver's error floor. Interference may also have other deleterious effects on communications. For example, interference may lower capacity in a communication system, decrease the region of coverage, and/or decrease maximum data rates. For these reasons, a reduction in interference can improve reception of selected signals while addressing the aforementioned limitations due to interference. These interferences take the following form when code division multiplexing is employed for a communication link, either with code division multiple access (as used in CDMA 2000, WCDMA, and related standards) or with time division multiple access (as used in EV-DO and related standards). A set of symbols is sent across a common time-frequency slot of the physical channel and separated using a set of distinct code waveforms, which are usually chosen to be orthogonal (or pseudo-orthogonal for reverse-link transmissions). The code waveforms typically vary in time, and these variations are introduced by a pseudo-random spreading code (PN sequence). The wireless transmission medium is characterized by a time-varying multipath profile that causes multiple time-delayed replicas of the transmitted waveform to be received, each replica having a distinct amplitude and phase due to path loss, absorption, and other propagation effects. As a result, the received code set is no longer orthogonal. The code space suffers from intra-channel interference within a base station as well as inter-channel interference arising from transmissions in adjacent cells. The most basic receiver architecture employed to combat these various effects is the well-known Rake receiver. The Rake receiver uses a channel-tracking algorithm to resolve the received signal energy onto various multipath delays. These delayed signals are then weighted by the associated complex channel gains (which may be normalized by path noise powers) and summed to form a single resolved signal, which exploits some of the path diversity available from the multipath channel. It is well known that the Rake receiver suffers from a significant interference floor, which is due to both self-interference from the base station of interest (or base stations, when the mobile is in a soft-handoff base station diversity mode) and multiple-access interference from all base stations in the coverage area. This interference limits the maximum data rates achievable by the mobiles within a cell and the number of mobiles that can be supported in the cell. Advanced receivers have been proposed to overcome the limitations of the Rake receiver. The optimal multi-user detector (MUD) has the best performance, but is generally too computationally complex to implement. MUD complexity increases exponentially with respect to the total number of active subchannels across the cell of interest and the interfering cells as well as the constellation size(s) of the subchannels. This complexity is so prohibitive that even efficient implementations based on the Viterbi algorithm cannot make it manageable in current hardware structures. Another approach is a properly designed linear receiver, which in many channel scenarios, is able to retain much of the optimal MUD performance, but with a complexity that is polynomial in the number of subchannels. The most common examples are the linear minimum mean squared error (LMMSE) receiver and the related decorrelating (or zero-forcing) receiver, which both require finding, or approximating, the inverse of a square matrix whose dimension is equal to the lesser between the number of active subchannels and the length (in samples) of the longest spreading code. Complexity can still be prohibitive with these receivers, because such a matrix inverse needs to be calculated (or approximated) for each symbol. These receivers depend not only on the spectral characteristics of the multipath fading channel (which could be slowly time varying), but also on the time-varying spreading codes employed on the subchannels over each symbol. Thus, these receivers vary at the symbol rate even if the channel varies much more slowly. An alternative approach currently under development for advance receivers sidesteps the need to invert a matrix for each symbol. It accomplishes this by employing a PN-averaged LMMSE (PNA-LMMSE) receiver that assumes the PN code is random and unknown at the receiver (at least for determining the correlation matrix). While this receiver is generally inferior to the LMMSE approach, it has the advantage of not having to be implemented directly, because it is amenable to adaptive (or partially adaptive) implementations. The advantages of an adaptive implementation over a direct implementation include reduced complexity and the fact that the additive noise power (i.e., background RF radiation specific to the link environment, noise in the receiver's RF front end, and any processing noise such as noise due to quantization and imperfect filtering) does not have to be estimated. However, these advantages incur the costs associated with adaptive filters (e.g., performance and adaptation rate). Note that a direct implementation without knowledge of the noise power modifies the LMMSE and PNA-LMMSE receivers into the corresponding decorrelating (or zero-forcing) receivers that arise from taking the background noise power to be zero when deriving the LMMSE and PNA-MMSE receivers. Another method for further reducing complexity is to iteratively approximate the matrix-inverse functionality of the LMMSE receiver without explicitly calculating the inverse. Receivers of this type employ multistage interference cancellation. One particular type is known as parallel interference cancellation (PIC), and is motivated by well-known iterative techniques of quadratic minimization. In each stage of PIC, the data symbols of the subchannels are estimated. For each subchannel, an interference signal from the other subchannels is synthesized, followed by interference cancellation that subtracts the synthesized interference from each subchannel. The interference-cancelled subchannels are then fed to a subsequent PIC stage. Ideally, within just a few stages (i.e., before the complexity grows too large), the performance rivals that of the full linear receiver using a matrix inverse. PIC can be implemented in various modes depending on what types of symbol estimates are used for interference cancellation. In a soft-cancellation mode, PIC does not exploit additional information inherent in the finite size of user constellations. That is, estimates of data symbols are not quantized to a constellation point when constructing interference signals. However, in some multiple-access schemes, the user constellations may be known (e.g., in an EV-DO link or in a WCDMA link without HSDPA users) or determined through a modulation classifier. In such cases, it is possible for PIC to be implemented in a hard-cancellation mode. That is, estimates of data symbols are quantized to constellation points (i.e., hard decisions) when constructing the interference signal. In a mixed-cancellation mode, PIC employs a soft decision on each symbol whose constellation is unknown, and either a soft or hard decision on each symbol whose constellation is known, depending on how close the soft estimate is to the hard decision. Such a mixed-decision PIC typically outperforms both the soft-decision PIC and the hard-decision PIC. Moreover, it can also substantially outperform the optimal LMMSE receiver and promises even greater performance gains over PNA-LMMSE approaches currently under development for advanced receivers. The performance of soft-decision PIC is bounded by the optimal LMMSE. In view of the foregoing background, embodiments of the present invention may provide a generalized interference-canceling receiver for canceling intra-channel and inter-channel interference in coded, multiple-access, spread-spectrum transmissions that propagate through frequency-selective communication channels. Receiver embodiments may employ a designed and/or adapted soft-weighting subtractive cancellation with a stabilizing step-size and a mixed-decision symbol estimator. Receiver embodiments may be designed, adapted, and implemented explicitly in software or programmed hardware, or implicitly in standard Rake-based hardware, either within the Rake (i.e., at the finger level) or outside the Rake (i.e., at the subchannel symbol level). Embodiments of the invention may be employed in user equipment on the forward link and/or in a base station on the reverse link. Some embodiments of the invention address the complexity of the LMMSE approach by using a low-complexity iterative algorithm. Some embodiments of the invention in soft-mode may be configured to achieve LMMSE performance (as contrasted to the lesser-performing PNA-LMMSE) using only quantities that are easily measured at the receiver. Some embodiments address the sub-optimality of the LMMSE and PNA-LMMSE approaches by using an appropriately designed mixed-decision mode and may even approach the performance of an optimal multi-user detector. In some embodiments, stabilizing step sizes may be used to enhance stability of various PIC approaches. Some embodiments may employ symbol-estimate weighting to control convergence of various PIC approaches. Some embodiments of the invention address the limitation of various PIC approaches to binary and quaternary phase shift keying in mixed-decision mode by being configurable to any subchannel constellation. Some embodiments of the invention address the difficulty of efficiently implementing various PIC approaches in hardware by using a modified Rake architecture. Some embodiments of the invention address the so-called “ping-pong effect” (i.e., when the symbol error rate oscillates with iteration) in various PIC approaches by pre-processing with a de-biasing operation when making symbol estimates. In one embodiment of the invention, an iterative interference canceller comprises a weighting means configured for applying at least one symbol weight to the input symbol decisions, a stabilizing step size means configured for applying a stabilizing step size to an error signal, and a mixed-decision processing means. The mixed-decision processing means may include, by way of example, but without limitation, a combination of hardware and software configured to produce soft and/or hard symbol estimates, and may be known as a decision device or a symbol estimator. The stabilizing step size means may include, by way of example, but without limitation, any combination of hardware and software configured to scale an error signal with a scaling factor that may be used for controlling convergence in an iterative canceller. For example, the stabilizing step size means may include a signal processor configured to calculate at least one stabilizing step size and a multiplier for scaling an error signal with the step size. The weighting means may include, by way of example, but without limitation, a weight-calculation means configured for producing symbol weights, and a multiplier configured for multiplying symbol estimates by the weights. The weight-calculation means may include, by way of example, but without limitation, any combination of hardware and software configured to calculate symbol weights from a function employing a merit of at least one input symbol decision. In one embodiment, the merit may comprise an average ratio of signal power to interference-plus-noise power (or a function thereof). In another embodiment, the merit may be a function of input symbol decisions and proximity of those input symbol decisions to a nearby constellation point. In this case, the weight-calculation means may employ time-series averaging for calculating the proximity as a statistical average. In yet another embodiment, the weight-calculation means may include a signal processing means configured to perform statistical signal processing for estimating the average ratio of signal power to interference-plus-noise power. Such statistical signal processing may employ error-vector magnitude calculations. Embodiments of the invention may be employed in any receiver configured to support one or more CDMA standards, such as (1) the “TIA/EIA-95-B Mobile Station-Base Station Compatibility Standard for Dual-Mode Wideband Spread Spectrum Cellular System” (the IS-95 standard), (2) the “TIA/EIA-98-C Recommended Minimum Standard for Dual-Mode Wideband Spread Spectrum Cellular Mobile Station” (the IS-98 standard), (3) the standard offered by a consortium named “3rd Generation Partnership Project” (3GPP) and embodied in a set of documents including Document Nos. 3G TS 25.211, 3G TS 25.212, 3G TS 25.213, and 3G TS 25.214 (the WCDMA standard), (4) the standard offered by a consortium named “3rd Generation Partnership Project 2” (3GPP2) and embodied in a set of documents including “TR-45.5 Physical Layer Standard for cdma2000 Spread Spectrum Systems,” the “C.S0005-A Upper Layer (Layer 3) Signaling Standard for cdma2000 Spread Spectrum Systems,” and the “C.S0024 CDMA2000 High Rate Packet Data Air Interface Specification” (the CDMA2000 standard), (5) Multi-Code CDMA systems, such as High-Speed-Downlink-Packet-Access (HSDPA), and (6) other CDMA standards. Receivers and cancellation systems described herein may be employed in subscriber-side devices (e.g., cellular handsets, wireless modems, and consumer premises equipment) and/or server-side devices (e.g., cellular base stations, wireless access points, wireless routers, wireless relays, and repeaters). Chipsets for subscriber-side and/or server-side devices may be configured to perform at least some of the receiver and/or cancellation functionality of the embodiments described herein. These and other embodiments of the invention are described with respect to the figures and the following description of the preferred embodiments. Embodiments according to the present invention are understood with reference to the schematic block diagrams of Various functional elements or steps, separately or in combination, depicted in the figures may take the form of a microprocessor, digital signal processor, application specific integrated circuit, field programmable gate array, or other logic circuitry programmed or otherwise configured to operate as described herein. Accordingly, embodiments may take the form of programmable features executed by a common processor or discrete hardware unit. The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. First the invention will be described as it applies to a forward-link channel, and then extended to include reverse-link channels. The following formula represents an analog baseband signal received at a mobile from multiple base stations, each with its own multipath channel, -
- (0,T) is the symbol interval;
- B is the number of modeled base stations and is indexed by the subscript (s) which ranges from (0) to (B−1); here, and in the sequel, the term “base stations” will be employed loosely to include cells or sectors;
- L
_{(s) }is the number of resolvable (or modeled) paths from base station (s) to the mobile; - α
_{(s),l }and τ_{(s),l }are the complex gain and delay, respectively, associated with the l-th path of base station (s); - K
_{(s) }is the number of active users or subchannels in base station (s) that share a channel via code-division multiplexing; these users or subchannels are indexed from 0 to K_{(s)}−1; - u
_{(s),k}(t) is a code waveform (e.g., spreading waveform) of base station (s) used to carry the k^{th }user's symbol for that base station (e.g., a chip waveform modulated by a user-specific Walsh code and covered with a base-station specific PN cover); - b
_{(s),k }is a complex symbol transmitted for the k^{th }user or subchannel of base station (s); - and w(t) is zero-mean complex additive noise that contains both thermal noise and any interference whose structure is not explicitly modeled (e.g., inter-channel interference from unmodeled base stations and/or intra-channel interference from unmodeled paths).
Typically, a user terminal (e.g., a handset) is configured to detect only symbols transmitted from its serving base station (e.g., the symbols from base station ( Intra-channel interference typically occurs when multiple users are served by a given base station (i.e., a serving base station). Even if the users' transmitted code waveforms are orthogonal, multipath in the transmission channel causes the codes to lose their orthogonality. Inter-channel interference is caused by transmissions from non-serving base stations whose signals contribute to the received baseband signal y(t). The combined signal is resolved onto the users' code waveforms by correlative despreading, which comprises multiplying A symbol estimator comprises scaling blocks A plurality K The soft weights can be regarded as a confidence measure related to the accuracy of a decision, or symbol estimate. For example, a high confidence weight relates to a high certainty that a corresponding decision is accurate. A low confidence weight relates to a low certainty. Since the soft weights are used to scale decisions, low-valued weights reduce possible errors that may be introduced into a calculation that relies on symbol estimates. In one embodiment of the invention, the weights γ The SINR (and thus, the soft weights) may be evaluated using techniques of statistical signal processing, including techniques based on an error-vector magnitude (EVM). Alternatively, a pilot-assisted estimate of the broadband interference-plus-noise floor, together with a user specific estimate of the signal-plus-interference-plus-noise floor, may be used to estimate the SINR values. In another embodiment of the invention, the weights γ In this embodiment, the weights γ In some embodiments, both Equation 3 and Equation 4 may be used in a receiver to calculate soft weights. Some embodiments of the invention may provide for subset selection to force one or more of the weights to zero. Such embodiments may be expressed as adaptations to Equation 3 and/or Equation 4 expressed by
Weighted symbol estimates γ In synthesizing module
Canceller In an alternative embodiment, cancellation may be performed with only a subset of the constituent channels. In each base station, only those constituent signals being used for cancellation may be used to synthesize the estimated receive signal for base station (s). Thus, {tilde over (y)} The interference-cancelled signals produced by the canceller shown in Interference cancelled signals z
Interference cancelled signals z Input constituent signals {tilde over (y)} Rake despreading, such as described with respect to the exemplary Rake despreading module A diagonal soft-weighting matrix may be defined as
^{[i]} =[{circumflex over (b)} _{(0),0} ^{[i]} , . . . , {circumflex over (b)} _{(0),K} _{ (0) } _{−1} ^{[i]} | . . . |{circumflex over (b)} _{(B−1),0} ^{[i]} , . . . , {circumflex over (b)} _{(B−1),K} _{ (B−1) } _{−1} ^{[i]}]^{T}. Equation 15
The weighted symbol estimates are expressed as Γ ^{[i]} = q−RΓ^{[i]} b ^{[i]}, Equation 16
where, q=[q_{(0),0} , . . . , q _{(0),K} _{ (0) } _{−1} | . . . |q _{(B−1),0} , . . . , q _{(B−1,K} _{ (B−1 } _{−1}]^{T }and Equation 17
{tilde over ( q)}^{[i]} =[{tilde over (q)} _{(0),0} ^{[i]} , . . . , {tilde over (q)} _{(0),K} _{ (0) } _{−1} ^{[i]} | . . . |{tilde over (q)} _{(B−1),0} ^{[i]} , . . . , {tilde over (q)} _{(B−1),K} _{ (B−1) } _{−1} ^{[i]}]^{T}. Equation 18
The values of q ^{[i]} represent the despread signals, such as described with respect to q)}^{[i]} are represented by Equation 13, and R is a square matrix whose elements are correlations between the users' received code waveforms. In 931 and a subtraction module 932.
A global index κε{0, 1, . . . , K−1} with ^{[i+1]} =FΓ ^{[i]+μ} ^{[i]}( q−{tilde over (q)} ^{[i]}). Equation 20The choice of F allows interference cancellation after despreading to mimic interference cancellation prior to despreading for either user constituents or finger constituents. For user constituents, F=I. For finger constituents, F is a block-diagonal matrix with a plurality B of diagonal blocks, wherein an s The stabilizing step size μ _{(0),0} , . . . , b _{(0),K} _{ (0) } _{−1} | . . . |b _{(B−1),0} , . . . , b _{(B−1),K} ^{(B−1)} _{−1}]^{T}, Equation 22
are jointly complex normal random variables with mean R b and covariance Γ^{[i]}R (i.e., q|b is distributed as CN(Rb; R)). If it is approximated that q|{tilde over (b)} ^{[i+1]} is distributed as CN(R{tilde over (b)} ^{[i+1]};R), where {tilde over (b)} ^{[i+1]} and its dependence on μ^{[i]} are given by Equation 20, then the value of μ^{[i]} that gives the maximum-likelihood soft estimate for {tilde over (b)} ^{[i+1]} is
Different formulations of the step-size may be used within the same IIC. For example, a step size based on Equation 24 may be used in a sequence of ICUs and Equation 23 may be used in the last ICU of the sequence. The Equations 23 and 24 may be adapted for cases in which there is no soft weighting (i.e., when Γ
A combiner The norm-square of A synthesized received signal is generated In an alternative embodiment, the stabilizing step size may be derived from the multipath channel gains, The de-biasing constant may be expressed by
The map Ψ A mixed-decision map Ψ An alternative mixed-decision map Ψ Both the average SINR and instantaneous approaches are applicable to any known constellation; they need not be restricted to BPSK, QPSK, or even QAM. Either of these mixed-decision approaches may be performed with the additional constraint that the receiver knows only the constellation employed for a subset of the active codes. Such situations may arise in EV-DO and HSDPA networks. In such cases, the receiver may use soft decisions for codes employing an unknown modulation. Those skilled in the art will understand that a modulation classification of these codes may be performed, which may be particularly useful in systems wherein all interfering codes share the same unknown constellation. The following algorithm, which is illustrated in Algorithm 1: Purpose: Estimate the -
- {circumflex over (
__b__)}^{[i]}is in Equation 31 -
__q__is in Equation 17 - R is in Equation 19
- F is I or as in Equation 21
- Γ
^{[i]}is in Equation 14 with elements defined in Equation 3-Equation 5 - μ
^{[i]}is defined in Equation 23-Equation 27 - Ψ maps each argument to a complex number to implement de-biasing as in Equation 28-Equation 30 and then symbol estimation as in Equation 32-Equation 36
Initializations: {circumflex over (__b__)}^{[−1]}=__0__, a K×1 zero vector - Γ
^{[−1]}=I, a K×K identity matrix - μ
^{[−1]}=1 Iterations: Index i=−1, 0, 1, . . . , M−1, where M is the number of times to iterate the succeeding update equation
- {circumflex over (
Although embodiments of the invention are described with respect to forward-link channels, embodiments may be configured to operate in reverse-link channels. In the reverse link, different users' transmissions experience different multipath channels, which requires appropriate modifications to Rake processing and signal synthesis. For example, a front-end processor may incorporate one Rake for every user in every base station rather than a single Rake per base station. Similarly, a separate multipath channel emulator may be employed for imparting multipath delays and gains to each user's signal. Accordingly, the number of constituent finger signals will equal the sum over the number of multipath fingers per user per base station, rather than the sum over the number of multipath fingers per base station. It is clear that this algorithm may be realized in hardware or software and there are several modifications that can be made to the order of operations and structural flow of the processing. Those skilled in the art should recognize that method and apparatus embodiments described herein may be implemented in a variety of ways, including implementations in hardware, software, firmware, or various combinations thereof. Examples of such hardware may include Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), general-purpose processors, Digital Signal Processors (DSPs), and/or other circuitry. Software and/or firmware implementations of the invention may be implemented via any combination of programming languages, including Java, C, C++, Matlab™, Verilog, VHDL, and/or processor specific machine and assembly languages. Computer programs (i.e., software and/or firmware) implementing the method of this invention may be distributed to users on a distribution medium such as a SIM card, a USB memory interface, or other computer-readable memory adapted for interfacing with a consumer wireless terminal. Similarly, computer programs may be distributed to users via wired or wireless network interfaces. From there, they will often be copied to a hard disk or a similar intermediate storage medium. When the programs are to be run, they may be loaded either from their distribution medium or their intermediate storage medium into the execution memory of a wireless terminal, configuring an onboard digital computer system (e.g., a microprocessor) to act in accordance with the method of this invention. All these operations are well known to those skilled in the art of computer systems. The functions of the various elements shown in the drawings, including functional blocks labeled as “modules” may be provided through the use of dedicated hardware, as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be performed by a single dedicated processor, by a shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” or “module” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor DSP hardware, read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. Other hardware, conventional and/or custom, may also be included. Similarly, the function of any component or device described herein may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context. The method and system embodiments described herein merely illustrate particular embodiments of the invention. It should be appreciated that those skilled in the art will be able to devise various arrangements, which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are intended to be only for pedagogical purposes to aid the reader in understanding the principles of the invention. This disclosure and its associated references are to be construed as applying without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Patent Citations
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